# Explainable Planning

arXiv: https://arxiv.org/abs/1709.10256 · HTML: https://mlsanity.com/paper/1709.10256/

## Key facts

- Cited by 40 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Sep 2017.

## Built on by (most cited first, 30 of 40)

- [Explainable AI (XAI): A Systematic Meta-Survey of Current Challenges and Future Opportunities](https://mlsanity.com/paper/2111.06420/) (2021)
- [What Do We Want From Explainable Artificial Intelligence (XAI)? -- A Stakeholder Perspective on XAI and a Conceptual Model Guiding Interdisciplinary XAI Research](https://mlsanity.com/paper/2102.07817/) (2021)
- [Explainable Artificial Intelligence for Autonomous Driving: A Comprehensive Overview and Field Guide for Future Research Directions](https://mlsanity.com/paper/2112.11561/) (2021)
- [Logic-Based Explainability in Machine Learning](https://mlsanity.com/paper/2211.00541/) (2022)
- [To trust or not to trust an explanation: using LEAF to evaluate local linear XAI methods](https://mlsanity.com/paper/2106.00461/) (2021)
- [Explainable Reinforcement Learning for Broad-XAI: A Conceptual Framework and Survey](https://mlsanity.com/paper/2108.09003/) (2021)
- [Contrastive Explanations of Plans Through Model Restrictions](https://mlsanity.com/paper/2103.15575/) (2021)
- [Directive Explanations for Actionable Explainability in Machine Learning Applications](https://mlsanity.com/paper/2102.02671/) (2021)
- [Challenges in Human-Agent Communication](https://mlsanity.com/paper/2412.10380/) (2024)
- [A Research Agenda for AI Planning in the Field of Flexible Production Systems](https://mlsanity.com/paper/2112.15484/) (2021)
- [Logic programming for deliberative robotic task planning](https://mlsanity.com/paper/2301.07550/) (2023)
- [Can LLMs Fix Issues with Reasoning Models? Towards More Likely Models for AI Planning](https://mlsanity.com/paper/2311.13720/) (2023)
- [Enabling MCTS Explainability for Sequential Planning Through Computation Tree Logic](https://mlsanity.com/paper/2407.10820/) (2024)
- [Explaining Preference-driven Schedules: the EXPRES Framework](https://mlsanity.com/paper/2203.08895/) (2022)
- [Efficiently Explaining CSPs with Unsatisfiable Subset Optimization (extended algorithms and examples)](https://mlsanity.com/paper/2303.11712/) (2023)
- [Counterfactual Influence in Markov Decision Processes](https://mlsanity.com/paper/2402.08514/) (2024)
- [Trustworthy AI and Robotics and the Implications for the AEC Industry: A Systematic Literature Review and Future Potentials](https://mlsanity.com/paper/2109.13373/) (2021)
- [Rule-based Shielding for Partially Observable Monte-Carlo Planning](https://mlsanity.com/paper/2104.13791/) (2021)
- [Causal Explanations for Sequential Decision Making Under Uncertainty](https://mlsanity.com/paper/2205.15462/) (2022)
- [Conflict-Based Search for Explainable Multi-Agent Path Finding](https://mlsanity.com/paper/2202.09930/) (2022)
- [Understanding a Robot's Guiding Ethical Principles via Automatically Generated Explanations](https://mlsanity.com/paper/2206.10038/) (2022)
- [Fairness in Multi-Agent Planning](https://mlsanity.com/paper/2212.00506/) (2022)
- [Safe Explicable Planning](https://mlsanity.com/paper/2304.03773/) (2023)
- [On Generating Monolithic and Model Reconciling Explanations in Probabilistic Scenarios](https://mlsanity.com/paper/2405.19229/) (2024)
- [Learning Visually Grounded Domain Ontologies via Embodied Conversation and Explanation](https://mlsanity.com/paper/2412.09770/) (2024)
- [Mind the Prompt: Self-adaptive Generation of Task Plan Explanations via LLMs](https://mlsanity.com/paper/2604.21092/) (2026)
- [Argument Schemes and Dialogue for Explainable Planning](https://mlsanity.com/paper/2101.02648/) (2021)
- [Efficiently Explaining CSPs with Unsatisfiable Subset Optimization](https://mlsanity.com/paper/2105.11763/) (2021)
- [Encoding Compositionality in Classical Planning Solutions](https://mlsanity.com/paper/2107.05850/) (2021)
- [Beyond Value: CHECKLIST for Testing Inferences in Planning-Based RL](https://mlsanity.com/paper/2206.02039/) (2022)

## Abstract (arXiv)

As AI is increasingly being adopted into application solutions, the challenge of supporting interaction with humans is becoming more apparent. Partly this is to support integrated working styles, in which humans and intelligent systems cooperate in problem-solving, but also it is a necessary step in the process of building trust as humans migrate greater responsibility to such systems. The challenge is to find effective ways to communicate the foundations of AI-driven behaviour, when the algorithms that drive it are far from transparent to humans. In this paper we consider the opportunities that arise in AI planning, exploiting the model-based representations that form a familiar and common basis for communication with users, while acknowledging the gap between planning algorithms and human problem-solving.